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DTSTART:20260308T100000
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DTSTART:20261101T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T160000
DTEND;TZID=America/Los_Angeles:20260527T170000
DTSTAMP:20260531T112040
CREATED:20260527T235020Z
LAST-MODIFIED:20260527T235020Z
UID:33656-1779897600-1779901200@www.ayclogic.com
SUMMARY:Scratch 2 - Wed 4:00 PM - Julian
DESCRIPTION:What We Did:\n\nContinued our Cheese Chase project.\n\nHomework:\n\nCreate a ghost sprite\nMake the ghost sprite chase the player\nHINT: Make the ghost look at the player then move 10 steps\n\nIf you have any questions feel free to email me at jsaroufim@ayclogic.com
URL:https://www.ayclogic.com/event/scratch-2-wed-400-pm-julian-5/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T170000
DTEND;TZID=America/Los_Angeles:20260527T180000
DTSTAMP:20260531T112040
CREATED:20260528T005512Z
LAST-MODIFIED:20260528T010958Z
UID:33659-1779901200-1779904800@www.ayclogic.com
SUMMARY:Scratch 1 - Wed 5:00 PM - Julian
DESCRIPTION:What We Did:\n\nWent over our second problem on the practice Scratch test.\n\nHomework:\n\nSubmit the practice final test if you haven’t already: https://docs.google.com/forms/d/1D2V4Z65i10Mj8oNbuCVY1ShidaLwd4178U-LZrSHs5k/viewform?edit_requested=true\nHow to submit the link to your Scratch project:\n\n1) Click on Share in your Scratch project (the orange button at the top)\n2) Click on copy link then copy the short link it gives you (you can just click on copy link again)\n3) Paste the link into the google form\, ask your parents if you don’t know how to paste or are confused about any of these things\n\n\n\nIf you have any questions feel free to email me at jsaroufim@ayclogic.com
URL:https://www.ayclogic.com/event/scratch-1-wed-500-pm-julian-12/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T180000
DTEND;TZID=America/Los_Angeles:20260527T190000
DTSTAMP:20260531T112040
CREATED:20260528T015404Z
LAST-MODIFIED:20260528T015404Z
UID:33663-1779904800-1779908400@www.ayclogic.com
SUMMARY:Scratch 2 - Wed 6:00 PM - Julian
DESCRIPTION:What We Did:\n\nContinued our Spaceship project.\n\nHomework:\n\nMake the bomb powerup\, it should work the exact same as the fireball powerup except it increases your bomb count.\n\nIf you have any questions feel free to email me at jsaroufim@ayclogic.com
URL:https://www.ayclogic.com/event/scratch-2-wed-600-pm-julian-4/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T190000
DTEND;TZID=America/Los_Angeles:20260527T200000
DTSTAMP:20260531T112040
CREATED:20260528T025100Z
LAST-MODIFIED:20260528T025100Z
UID:33667-1779908400-1779912000@www.ayclogic.com
SUMMARY:7 PM - Python OOP - Joel
DESCRIPTION:What we learned:\n\nFinished the school system\n\nHomework:\n\nCreate a book class with the attributes title\, id\, author\, year\, publish year\nThere should be methods add book\, find book by id\, find book with name\, list all books (Main class called LibrarySystem)
URL:https://www.ayclogic.com/event/7-pm-python-oop-joel-12/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260527T190000
DTEND;TZID=America/Los_Angeles:20260527T200000
DTSTAMP:20260531T112040
CREATED:20260528T032207Z
LAST-MODIFIED:20260528T032207Z
UID:33669-1779908400-1779912000@www.ayclogic.com
SUMMARY:7 PM – AI/ML – Darin
DESCRIPTION:Today’s Activities:\n\n\n\nFinished the Single Digit Classifier project.\n\nHomework:\nFor Rexford\, choose the dataset for your project (has to be multiclass\, and catch up the below) \n\nHW 1: \n\nChoosing what you want to classify — Can be anything in real life\, the only thing is you have to make sure the data exists on Kaggle. \nThe project has to be able to classify at least 5 different categories. \nWhat we did last week: \n\nSet up the labelling function\nVerify Dataloader works with .show_batch() (reference old code for how to do this\, it will depend on how you extract labels\, whether its via path or filename)\nRun training with a vision_learner.\nUse the proper imports (fast ai 2.7.19)\nUse lr.find() to get the proper learning rate\, then run finetuning with the discovered value (you must only call fine_tune once!)Add the following into the vision learner to ensure lr_find works!:\npath=Path(“/kaggle/working”)\,\nmodel_dir=”models”\,\nExport the file as a .pt file onto your computer!\n\nWhat you have to do next week: \n\nUse your exported .pt file on streamlit and deploy on the website https://streamlit.io/\n\n\nHW 2 (if you haven’t done so already): \nDownload the following CSV file: https://drive.google.com/file/d/1kP6A9y0UBssOg3Exunv9Mnmilb0657Sh/view \nThen do the following in a new notebook called WED-7PM-PandasHW1: \n\nLoad the data\nShow only the Channel and Subscribers columns\nFind channels with more than 2000 subscribers\nFor this you can use something like this:  \ndf[df[“Math”] > 80]\,but of course for this data.\nAdd a new column Subs_per_Video\nAnswer the question in Markdown\, which channel is the most efficient?\n\n\nNotes:\nYou can reach me at ddjapri@ayclogic.com. \nAll class notes can be found here.
URL:https://www.ayclogic.com/event/7-pm-ai-ml-darin-29/
CATEGORIES:AI/ML,Python Class
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